Abstract This paper aims to define design patterns specifically for data ingestion techniques within cloud-based architectures, addressing the challenges associated with high-volume data processing. The approach utilizes a flexible, metadata-driven framework that enhances adaptability and ease of use. This framework supports both incremental and full refresh methods, allowing for seamless changes to ingestion types, schema updates, table additions, and the incorporation of new data sources with minimal intervention from data engineers. The proposed design patterns were validated through experiments conducted on the Azure and Google Cloud platforms. The experiments demonstrate that the proposed design patterns significantly reduce data ingestion time, showcasing their effectiveness in managing high-volume data ingestion. This paper contributes to the field of data management by presenting a comprehensive definition of design patterns tailored for data ingestion in cloud-based architectures, effectively addressing key challenges in high-volume data processing.

Extensive research has explored business process model similarity. This work aims to explore the coherence and structure of the existing studies, with a focus on evaluating behavioural similarity. We conducted a systematic review of the literature on process model similarity, with a focus on two main measurement approaches: trace-based and model-based similarity. Based on our review of 99 papers, we developed a three-dimensional framework and conducted a quantitative comparison of selected similarity measures to deepen our analysis. Our findings provide valuable insights that could enhance the assessment of process model similarity, particularly with an emphasis on behaviour. The review process followed a six-phase systematic methodology, from the identification of relevant keywords to the creation of bibliographic maps for a visual representation of the findings. these insights serve as a foundation for future research and practical applications within the field.

Gaps in current numerical models highlight challenges in accurately simulating photovoltaic (PV) systems, particularly their sensitivity to weather forecast outputs and the impact of environmental factors such as wind speed and humidity. These factors introduce discrepancies between simulated and real-life PV system performance. This study investigates the potential of open-source PV physical models, leveraging pvlib-python, to address these challenges. The research focuses on enhancing the accuracy of these models to generate synthetic data that closely replicate real-world conditions. Using the single diode equation implemented in pvlib-python, DC power production data from a real-life PV module is simulated, with initial discrepancies identified and mitigated through the integration of Huld model. This integration aims to improve the accuracy of synthetic data, thereby bridging the gap between simulated and actual performance metrics. The efficacy of the enhanced predictive models is evaluated using historical field measurements from a PV system testbed, demonstrating advancements in simulating PV system behaviors with enhanced accuracy and reliability. The results unveil a significant enhancement in synthetic data generation using PVLib and Huld model.

The emergence of novel technologies is significantly contributing to the study of new approaches to the development of energy Cyber-Physical systems. Technologies like IoT, Big Data, Machine Learning and AI are becoming so tightly coupled to be considered as a singular technological framework. Such a framework is at the basis of the Digital Twin technology, which allows to interact with a real-world asset by referring to its digital counterpart. Being potentially applied to any kind of physical asset, the Digital Twin approach can create a synergy for a comprehensive modeling, monitoring, and forecasting of the same asset. In the energy domain, Digital Twins can facilitate the decision making by providing interactive dashboards to the stakeholders within a metaverse instance. These dashboards can support what-if analyses, forecasts on energy demand and production, and remote control of the physical assets to optimize their performance. This paper proposes a framework for developing a Cyber-Physical solution in the energy domain. The framework consists of three building blocks called Physical Asset, Data Infrastructure, and Digital Twin. The Physical Asset and the Digital Twin blocks correspond to the typical counterparts of a Cyber-Physical system, whilst the Data Infrastructure is delegated to support their synchronization and/or interaction. To assess the proposed framework, a real testbed was developed as Cyber-Physical system to implement the digital twin of a smart photovoltaic station.

Digital twins are increasingly being implemented in smart cities, where they are designed to provide 3D digital representations for near-real time interactivity and status feedback of the twinned physical assets. The ability to provide accurate predictions in what-if scenarios plays a key role in driving the advances in resources optimization and risk mitigation. To this end, prediction models require large datasets to train, and such datasets are usually scarce at local level. In this paper, we propose a generative AI-based workflow to infer domestic photovoltaic energy production data, based on a process that uses Generative Adversarial Networks to generate an enriched meteorological dataset. The generated meteorological data captures essential statistical properties of real data that can be later used to infer realistic energy production data points. The resulting output dataset is validated against a real photovoltaic system, highlighting the ability to deliver high-fidelity time series out of scarce input datasets.

Data spaces are driving smart city innovation by promoting interoperability, standardization, and predictive analytics, enabling proactive urban management and fostering innovation by creating a collaborative data ecosystem. The European Community is strongly promoting data spaces design initiatives. However, designers face complex challenges in complying with legal data regulations due to the lack of mature, unified solutions.This study aims to fill a gap in data space design literature by gathering key factors and scouting compliant implementations. It introduces European initiatives and their joint ventures, and identifies overlaps and lack of coordination between their proposals. Using these results, we formulate suggested actions and organize them into seven areas. For each given area, the most suitable European data space initiatives in terms of technical specifications and implementations are finally proposed to DS developers. The recommendations are intended to assist DS developers in identifying and navigating European DS design initiatives before selecting the most suitable proposal.

The rapid growth of data has created significant challenges in managing and leveraging data effectively. Data engineering has emerged as a crucial discipline to address these challenges, providing frameworks for efficient data management. Data Engineering Patterns (DEP) and Data Engineering Design Patterns (DEDP) offer standardized practices and best practice solutions for data engineering tasks like ETL. While various DEPs and DEDPs exist, the issue of high-volume data ingestion remains insufficiently addressed. This paper focuses on defining design patterns specifically for data ingestion techniques within cloud-based architectures, covering both incremental and full refresh methods. The proposed approach utilizes a flexible, metadata-driven framework to enhance adaptability and ease of use, allowing for seamless changes to the ingestion type, schema updates, table additions, and incorporation of new data sources. Validated on the Azure cloud platform, the experiments demonstrate that the proposed design patterns significantly reduce data ingestion time, contributing to the field of data management by addressing key challenges in high-volume data processing.

Edge-powered dataspaces enhance time-sensitive applications, efficient bandwidth, security, and privacy, particularly for smart urban environment data spaces. In addition, designing optimized distributed edge-powered dataspaces that consider the shared data residing near the provider’s ease the management and control over the data. However, since distributed edge devices are heterogeneous, application deployment and management are typically challenging. Container virtualization is an important technology that addresses this problem, specifically on devices with limited resources. Scaling the number of nodes does not always guarantee adequate performance improvements. Therefore, it is crucial to study the effect of horizontal scalability on runtime, throughput, latency, and scaling the record and operation counts. In this study, the MongoDB database has been benchmarked using Yahoo! Cloud Serving Benchmark with a Docker Container of a single node and a Docker Swarm cluster of nine nodes scaled horizontally on Raspberry Pi. In addition, the study scales the record and operation counts to understand their effect in a similar environment.

The Energy System is a critical infrastructure (CI) classified within the realm of Cyber-Physical-Social Systems due to its integration of computerized control systems and society’s consumption patterns, interfacing with the physical entities involved in production, transmission, and distribution. As we navigate cutting-edge technologies, sustainable development, and the digitization of essential societal services, new challenges emerge for energy systems. One cutting-edge technology addressing the complexity of Cyber-Physical-Social CIs is the Digital Twins (DT). The present study delves into the implementation of DT within the Energy Systems domain. Search results from three prominent digital libraries (SCOPUS, WOS, IEEE) highlight the novelty of this subject, which has gained traction within the sector since 2018. The findings of our Systematic Literature Review (SLR) reveal notable enhancements in the resilience of Smart Energy Systems through the application of DT. This review thoroughly investigates the efficacy of DT utilization, explores its achievements, and applications, and confronts the associated challenges.

In response to the escalating global conflicts, predictive models have become extremely important for peacekeeping initiatives. The proliferation of “Hybrid Threats,” including terrorism and unconventional warfare, necessitates innovative strategies for enhancing peace and security. This paper outlines a collaborative effort with the United Nations Global Service Center (UNGSC) to develop a predictive tool for domestic conflicts in Africa, using diverse open-source datasets. The study employs data engineering, visualization, and integration techniques to explore commonalities and discrepancies among the datasets. Notably, data inconsistencies emerge, underscoring the significance of verifying information sources. While horizontal and vertical data integration possibilities are identified, challenges related to data anomalies and miscommunication are highlighted. To build a reliable predictive model, rigorous data analysis, expert insights, and a multidimensional approach are indispensable, ultimately contributing to conflict prevention and sustainable peacekeeping.

To mitigate the impact of COVID-19, the government has adopted various NPIs (Non-Pharmaceutical Interventions) ranging from wearing masks to social distancing. Over the due course of time, it has been proven that these NPIs are effective but assessing the effectiveness of the NPIs on the COVID-19 spread is still discussed. Till now, case confirmation and hospitalization have been incorporated as indicators to determine the success of NPIs. Here, we compare the effectiveness of two indicators such as the death rate and the number of cases, and their variation with human mobility, in assessing the effectiveness of NPIs to control the impact of COVID-19. The study includes the daily number of COVID-19 cases and deaths, Google Mobility Reports, and information on NPIs in 9 Italian regions for over 2 years from 2020. Similar considerations can be applied to other countries. The intent is to improve the method proposed by Wang et al. in 2020. Our findings suggest that in combination with human mobility, the death rate works better than the number of cases in assessing the effectiveness of NPIs. These findings can help policymakers formulate the best data-driven approaches for tackling confinement issues and structuring future scenarios in case of new outbreaks.

Green energy production is typically decentralized, and the ecosystem of production, transmission, and distribution differs significantly from centralized systems. Therefore, ensuring the resilience of green energy infrastructure demands a distinct approach, particularly regarding the security and safety aspects of these CIs. Green Energy CIs have less inherent protection, along with ancillary protection facilities compared to conventional power plants. This underscores the need to leverage AI to enhance the safety and security of green energy infrastructures, providing efficient and cost-effective solutions. This study aims to provide a comprehensive overview of AI implementations for enhancing the security and safety of green energy. Although this study is a work in progress, the present article will specifically delve into the resilience aspects of green energy infrastructures. Given the focus on AI implementation and data-driven solutions, we approach energy systems from a cyber-physical and societal perspective, emphasizing their broader impact on society. The ongoing study has unveiled significant improvements in resilience through the application of AI methods and data-driven models, such as machine learning, deep learning, neural networks, multiagent systems, big data, and data mining. Furthermore, we explore the challenges associated with integrating AI into green energy systems and investigate its various applications. This exploration aims to identify key features that will guide the development of novel approaches to enhancing the resilience of green energy systems through AI-based solutions for security and safety. Finally, results show a significant gap in the safety applications of AI. It received the least attention in the articles While the term ”safety” is frequently mentioned, even when the article’s primary focus is not on safety applications.

Digital Twins (DTs) are the novel paradigm for the development of Cyber-Physical systems. The state of art presents several use cases in different domains, and one of the most complex examples is represented by the Urban Digital Twin (UDT), which aims to virtualize urban assets (e.g., buildings, mobility infrastructures, energy grids, waste management facilities, etc.) and build advanced analysis and prediction services upon a city’s digital representation. UDTs represent a formidable example of system of systems, as they are structured into a hierarchy of interconnected DT instances that process and share a huge amount of data subject to different access and usage policies.Regarding data management in complex distributed scenarios, Data Spaces are an emerging paradigm that aims at building a secure and privacy-preserving infrastructure to pool, access, share, process and use data. As a matter of fact, existing DTs solutions (not only in the urban domain) do not present a clear software architecture characterization and, moreover, they pay little attention to the data management aspects.To bridge this gap, in this paper we present the Digital Twin Space, an architectural proposal that aims to instantiate Data Spaces into DTs according to the guidelines set by relevant international projects. We use the UDT as a running example and we validate the model in the case of a smartPV panel, showing that the model matches the real cyber-physical system.

Fetal biometry is considered the keystone in fetal well-being assessment. In particular, fetal growth curves built by means of ultrasound images and reference charts (defining the normal and pathological sizes for each biometric parameter and for each gestational age) are extensively adopted to track fetal sizes from the early phases of pregnancy up to delivery. In literature a large variety of reference charts are reported to consider the differences among different ethnic groups, but they are up to five decades old and they do not consider environmental factors such as foods, lifestyle, smoke, familial aspects, physiological and pathological variables, temporal parameters etc., which cannot be disregarded in a correct diagnosis. Therefore, current reference charts are rapidly becoming inadequate to support the melting pot of ethnic groups and lifestyles of our society, while customized reference charts can provide an accurate fetal assessment for the different fetal anthropometrical variables. Starting from a detailed analysis of the limits of classical reference charts, the paper presents a new method, based on multidimensional analysis for creating personalized fetal growth curves. A simple implementation, based on Open Source software and simulated data, shows the need of Big Data techniques in order to scale up the problem.